noorden 2008 arjan

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Research challenges

Arjan den Dekker and Xavier Bombois

Noorden, 9 October, 2008

Delft Center for Systems and Control


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Research profile DCSC

Fundamentals

Signals, systems and control

Fundamentals

Signals, systems and control

Automotive and intelligent

transportation systems

Automotive and intelligent

transportation systems

Mechatronics and microsystems

Mechatronics and microsystems

Physical Imaging systems and

adaptive optics

Physical Imaging systems and

adaptive optics

Sustainable industrial processes

Sustainable industrial processes


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Fundamentals:

• Uncertainty bounding in prediction error identificationMSc student: Chengwu Tong

Physical imaging systems:

• Model-based quantitative electron microscopypartner: University of Antwerp

• CONDOR: Model-based auto-tuning of electron microscope systems

partners:

• Experimental design for high-resolution cryoEM single-particle reconstruction

partner: Scripps Research Institute, CA, USA. MSc student: Pauline Vos

Ongoing research


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Ongoing research• Optimal statistical analysis of functional magnetic resonance data

partners: University of Antwerp, AMC Amsterdam

Miscellaneous:

• Well testing in the framework of System Identification

partner: Shell Research MSc student: Ivo Kuiper

• Psycho-physiological event detection from ECG and EEG

partner: Philips Research MSc student: Letian Wang

• Modeling and Control in Metabolic Systems Engineering

partner: Bioprocess Technology group (TNW) Dirk Vries


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Uncertainty bounding in PE identification



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Statistical inference


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Confidence regions

Research challenges

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People involved in research on Probabilistic Parameter Uncertainty Bounding

Dr.ir. Xavier Bombois(DCSC)

Prof. dr. ir. Paul Van den Hof(DCSC)

Dr.ir. Arjan den Dekker(DCSC)

Chengwu Tong (MSc student)

You???


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Scanning Transmission ElectronMicroscopy (STEM)

probe at sample

diffraction pattern (DP)

DP/ probe image

LOL

IL

sample

-deflector coils

DP image

PL

PrL

HAADFsensor

screen or CCD

FEG

CL

objective aperture

pointsource

UOL

z

x

x

FFP

• Several procedures need to be automated• Auto-alignment• Sample positioning• Sample analysis

• Several perturbations are present• Mechanical vibration• Thermal noise• Mechanical drift


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Microscope alignment

• Electron beams are focused with electromagnetic lenses• Lenses have sub-optimal behavior• This behavior can be influenced/altered

• The research is about:• Automatic optimization of the lens behavior• With Ronchigram: diffraction image• Challenges:

• low signal-to-noise ratio• non-linearities• multiple aberrations may yield the same result


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Control strategies for automaticalignment using the Ronchigram

• Identifying control parameters• shapes• angles• distances

• System identification• Control design


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People involved in EM research Dr.ir. Sara van der Hoeven

Dr. Arturo Tejada

Prof. dr. ir. Paul Van den Hof

Dr.ir. Arjan den Dekker

You..??


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Functional magnetic resonance imaging (fMRI)

Goal: detection of significant brain activity in response to a known stimulus

?

Method: apply statistical tests to voxel time series


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fMRI time series data

• Subject executes a (sensory, motoric or cognitive) task.

• Selected voxelsrepeatedly scanned through time.


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Modelling fMRI data


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Modelling fMRI data

•• StimulusStimulus•• ResponseResponse•• TrendTrend•• NoiseNoise

(low SNR)


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Modelling fMRI data


(low SNR)


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Modelling fMRI data


(low SNR)


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Modelling fMRI data


(low SNR)

Active or inactive? Active or inactive?

ThatThat’’s the question!s the question!


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Statistical hypothesis testing

How to determine whether or not a voxel is active?


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Shortcomings of the standard approach

Standard approach:

testing significance of activation related parameter(s) θ using standard statistical tools (e.g., t-test, F-test).

Standard approach is essentially dependent on • correct specifications of the HRF, • knowledge of correlation structure of the noise,• the assumption of Gaussian distributed data.

In practice, these conditions are usually not satisfied.


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Requirements for optimal inference

Optimal inference in fMRI based activation detection requires:

• An accurate model of the HRF• An accurate statistical model of the noise (statistical

distribution, temporal and spatial correlation)• Optimization of the input (stimulus) design• Advanced statistical hypothesis tests


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• HRF used is usually of fixed shape,

but… the validity of a one-size-fits all HRF is questionable!

Hemodynamic Response Function (HRF)

-10 -5 0 5 10 15 20 25


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Variability in the Hemodynamic Response

• Across Subjects• Across Sessions in a Single Subject• Across Brain Regions• Across Stimuli


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Challenges


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Challenges (continued)

• Optimize the experimental design:

Find the optimal stimuli for identification of the HRF and for the detection of brain activity.

• Develop advanced statistical hypothesis tests

Likelihood approach, Bayesian approach,…?


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People involved in fMRI research

Dr.ir. Xavier Bombois

Ir. Dirk Poot(PhD student University of Antwerp/DCSC)



You..??External partners:


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Supervision of MSc students

• (Two-)weekly progress meetings with supervisor(s)

• Monthly meetings of the KNIGHT club(KNowledge Is Gained Hanging Together)

KNIGHT Club meetings are informal research discussions attended by all MSc students, PhD students and postdocsthat work directly with Xavier and Arjan.

Fore more information, please visit http://www.dcsc.tudelft.nl/~adendekker/


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The next slides provide some extra information (and have not been presented during the Introduction days in Noorden).


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MSc project: Estimation of the step size of single molecular motors

From: Koster et al.. Nature 434, 671-674 (2005)


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Parameter estimation problem


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Possible approach


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People involved in “step size project”Ir. Dirk Poot(PhD student University of Antwerp/DCSC)

Prof. dr. ir. Paul Van den Hof(DCSC)

Dr.ir. Arjan den Dekker(DCSC)

You..?

External partner:(Molecular Biophysics Group, Kavli Institute of Nanoscience, DUT)


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The next two slides describe MSc projects in the field of Systems Biology.

For more information, please contactDirk Vries


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Input design for regulatory networks

Example of biological hybrid system.

Vibrio fischeri: marine bacteria. Luminescence is triggered (switched on/off) on the genetic, regulatory level and is only seen when population density reaches a certain level.

Goal: design input signal s.t. parametric estimation error is minimized

Assumptions:- noise corrupted (simulated) measurements- system modeled as hybrid system (discrete states: genes on/off, continuous states: signaling protein concentrations)

Challenges:(1) switch parameter reconstruction and further parameter estimation(2) input design (= control problem) s.t. issue (1) is fulfilled under input constraints


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Modeling and analysis of regulatory nutrient stress signaling in yeast

S. cerevisae is a yeast species and extensively used in industrial applications (beer, bakery products, biofuels). In these applications cells are subject to different nutrient environments due to non-ideal mixing.

Goal: model stress signalingmechanisms s.t. nutrient metabolisms are triggered with certain stochastic probability

Motivation: low quantities of signaling proteins ask for stochastic modeling approach

Challenge: stochastic hybrid modelingand analysis framework where mostly deterministic knowledge/ models are available


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People involved in Systems Biology research

Dr. ir. D. Vries

Dr. A. Tejada



You..??

External partner:Bioprocess Technology group TUD

noorden 2008 arjan

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